Telus EAP
Javier Mencia
2025-05-23
EDA
| Variable | Description | Mean | SD | Median | Min | Max |
|---|---|---|---|---|---|---|
| Age | Age in years | 40.14 | 11.21 | 39.00 | 18.00 | 70.00 |
| Height | Height in cm | 169.70 | 9.38 | 170.18 | 100.00 | 198.12 |
| Weight | Weight in pounds | 169.56 | 49.20 | 165.00 | 30.00 | 350.00 |
| Years in Canada | Duration living in Canada (years) | 15.62 | 20.94 | 0.00 | 0.00 | 70.00 |
| Years in US | Duration living in United States (years) | 15.48 | 18.67 | 0.00 | 0.00 | 59.00 |
| Current Work Ability (0-10) | Self-rated current work ability (0 = cannot work, 10 = best) | 7.38 | 1.93 | 8.00 | 1.00 | 10.00 |
| OHQ Score | Oxford Happiness Questionnaire score | 3.60 | 0.76 | 3.53 | 1.83 | 5.41 |
| WHOQOL Score | WHOQOL-BREF score | 3.52 | 0.69 | 3.59 | 1.73 | 5.00 |
| Negative Feelings | Frequency of Negative Feelings | 2.84 | 0.97 | 3.00 | 1.00 | 5.00 |
## # A tibble: 4 × 6
## country Age_Mean Height_Mean Weight_Mean Years_in_Canada Years_in_US
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 "" 31 167. 194. 11.7 9
## 2 "Canada" 43.0 170. 174. 39.6 0.192
## 3 "Other" 43.6 171. 164. 10.8 3.10
## 4 "United States" 36.9 170. 167. 0.851 32.4## work_hours overtime_hours
## Min. :42.00 Min. : 0.000
## 1st Qu.:48.00 1st Qu.: 0.000
## Median :50.00 Median : 1.000
## Mean :53.48 Mean : 6.273
## 3rd Qu.:60.00 3rd Qu.: 8.000
## Max. :72.00 Max. :75.000
## NA's :179 NA's :7## # A tibble: 4 × 5
## country work_hours_mean work_hours_sd overtime_mean overtime_sd
## <chr> <dbl> <dbl> <dbl> <dbl>
## 1 "" 55.5 10.6 14 18.4
## 2 "Canada" 56.2 9.56 3.77 9.61
## 3 "Other" 51 5.23 4.31 6.40
## 4 "United States" 53 9.19 8.64 13.3
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 3.00 5.00 7.75 10.00 48.00 14## # A tibble: 2 × 8
## country tenure_mean tenure_sd min q1 median q3 max
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Canada 8.60 8.14 0 1.75 5 15 26
## 2 United States 7.09 7.43 0 3 5 9 48Happiness and QoL EDA
| Â | OHQ Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 2.51 | -2.34 – 7.36 | 0.296 |
| work hours | 0.03 | -0.06 – 0.11 | 0.511 |
| country [United States] | 1.60 | -3.80 – 7.01 | 0.545 |
|
work hours × country [United States] |
-0.04 | -0.13 – 0.06 | 0.450 |
| Observations | 27 | ||
| R2 / R2 adjusted | 0.059 / -0.064 | ||
| Â | WHOQOL Score | ||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.61 | -0.05 – 7.26 | 0.053 |
| work hours | 0.00 | -0.06 – 0.07 | 0.949 |
| country [United States] | 0.50 | -3.58 – 4.57 | 0.802 |
|
work hours × country [United States] |
-0.01 | -0.08 – 0.06 | 0.736 |
| Observations | 27 | ||
| R2 / R2 adjusted | 0.025 / -0.102 | ||
## Analysis of Variance Table
##
## Model 1: WHOQOL_Score ~ work_hours + country
## Model 2: WHOQOL_Score ~ work_hours * country
## Res.Df RSS Df Sum of Sq F Pr(>F)
## 1 24 10.220
## 2 23 10.168 1 0.051306 0.116 0.7365
## Conditional item response (column) probabilities,
## by outcome variable, for each class (row)
##
## $OHQ_Score
## Pr(1) Pr(2) Pr(3)
## class 1: 0.3279 0.0000 0.6721
## class 2: 0.2023 0.7977 0.0000
## class 3: 0.0000 0.0000 1.0000
##
## $WHOQOL_Score
## Pr(1) Pr(2) Pr(3)
## class 1: 0.3279 0.6721 0
## class 2: 0.3619 0.6381 0
## class 3: 0.0000 0.0000 1
##
## $work_hours
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.6721 0.3279
## class 2: 0.7977 0.0000 0.2023
## class 3: 0.7000 0.2000 0.1000
##
## $overtime_hours
## Pr(1) Pr(2) Pr(3)
## class 1: 0.7760 0.0000 0.224
## class 2: 0.7607 0.2393 0.000
## class 3: 1.0000 0.0000 0.000
##
## Estimated class population shares
## 0.1653 0.4643 0.3704
##
## Predicted class memberships (by modal posterior prob.)
## 0.1481 0.4815 0.3704
##
## =========================================================
## Fit for 3 latent classes:
## =========================================================
## number of observations: 27
## number of estimated parameters: 26
## residual degrees of freedom: 1
## maximum log-likelihood: -73.48876
##
## AIC(3): 198.9775
## BIC(3): 232.6693
## G^2(3): 28.83653 (Likelihood ratio/deviance statistic)
## X^2(3): 32.91627 (Chi-square goodness of fit)
## ## # A tibble: 4 × 4
## lca_class age work_hours OHQ_Score
## <int> <dbl> <dbl> <dbl>
## 1 1 41.5 62 3.54
## 2 2 39.9 52.2 3.20
## 3 3 37.1 52.3 4.57
## 4 NA 39.7 NaN 3.61##
## Call:
## glm(formula = eap_used ~ OHQ_Score + WHOQOL_Score + work_hours +
## overtime_hours + gender + age + income, family = binomial,
## data = telus)
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -2.410e+02 1.561e+06 0.000 1
## OHQ_Score 5.598e+01 4.215e+05 0.000 1
## WHOQOL_Score -6.739e+01 9.635e+05 0.000 1
## work_hours 5.059e+00 9.691e+03 0.001 1
## overtime_hours -7.041e+00 1.190e+04 -0.001 1
## genderMale 6.575e+01 1.318e+05 0.000 1
## genderTransgender Male -1.043e+02 1.009e+06 0.000 1
## age -2.405e-01 9.005e+03 0.000 1
## income$30,000-$39,999 2.005e+01 2.894e+05 0.000 1
## income$40,000-$49,999 1.784e+02 4.745e+07 0.000 1
## income$50,000-$59,999 2.848e+01 5.108e+05 0.000 1
## income$60,000-$69,999 2.479e+01 4.813e+05 0.000 1
## income$70,000-$79,999 1.184e+01 2.644e+05 0.000 1
## income$80,000 or more NA NA NA NA
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 2.2325e+01 on 25 degrees of freedom
## Residual deviance: 9.5977e-10 on 13 degrees of freedom
## (153 observations deleted due to missingness)
## AIC: 26
##
## Number of Fisher Scoring iterations: 25## (Intercept) OHQ_Score WHOQOL_Score
## 0.000000e+00 2.055083e+24 0.000000e+00
## work_hours overtime_hours genderMale
## 1.574500e+02 0.000000e+00 3.600270e+28
## genderTransgender Male age income$30,000-$39,999
## 0.000000e+00 7.900000e-01 5.085040e+08
## income$40,000-$49,999 income$50,000-$59,999 income$60,000-$69,999
## 2.941558e+77 2.335478e+12 5.810071e+10
## income$70,000-$79,999 income$80,000 or more
## 1.381442e+05 NA
## ----------------------------------------------------
## Gaussian finite mixture model fitted by EM algorithm
## ----------------------------------------------------
##
## Mclust VEV (ellipsoidal, equal shape) model with 3 components:
##
## log-likelihood n df BIC ICL
## -89.54785 26 54 -355.0329 -355.0518
##
## Clustering table:
## 1 2 3
## 5 13 8
## ----------------------------------------------------
## Gaussian finite mixture model fitted by EM algorithm
## ----------------------------------------------------
##
## Mclust EII (spherical, equal volume) model with 5 components:
##
## log-likelihood n df BIC ICL
## -5298.208 150 150 -11348.01 -11353.77
##
## Clustering table:
## 1 2 3 4 5
## 24 16 45 6 59
## Conditional item response (column) probabilities,
## by outcome variable, for each class (row)
##
## $I.don.t.feel.particularly.pleased.with.the.way.I.am
## Pr(1) Pr(2) Pr(3)
## class 1: 0.2450 0.2308 0.5242
## class 2: 0.2859 0.5181 0.1961
## class 3: 0.6552 0.2418 0.1030
##
## $I.am.intensely.interested.in.other.people
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1238 0.2923 0.5839
## class 2: 0.2310 0.5178 0.2511
## class 3: 0.3786 0.3797 0.2417
##
## $I.feel.that.life.is.very.rewarding
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0776 0.9224
## class 2: 0.0542 0.6425 0.3033
## class 3: 0.2732 0.5500 0.1768
##
## $I.have.very.warm.feelings.towards.almost.everyone
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0771 0.1697 0.7532
## class 2: 0.1592 0.5900 0.2508
## class 3: 0.3116 0.5486 0.1398
##
## $I.rarely.wake.up.feeling.rested
## Pr(1) Pr(2) Pr(3)
## class 1: 0.3071 0.2772 0.4157
## class 2: 0.3208 0.4645 0.2147
## class 3: 0.5528 0.2070 0.2403
##
## $I.am.not.particularly.optimistic.about.the.future
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1847 0.1685 0.6468
## class 2: 0.1962 0.5544 0.2495
## class 3: 0.5842 0.2424 0.1734
##
## $I.find.most.things.amusing
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0460 0.2763 0.6777
## class 2: 0.0716 0.6250 0.3035
## class 3: 0.4117 0.4500 0.1384
##
## $I.am.always.committed.and.involved
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0155 0.1847 0.7998
## class 2: 0.0527 0.6239 0.3234
## class 3: 0.3101 0.4158 0.2741
##
## $Life.is.good
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0308 0.9692
## class 2: 0.0000 0.6237 0.3763
## class 3: 0.5141 0.3474 0.1385
##
## $I.don.t.think.that.the.world.is.a.good.place
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1835 0.2318 0.5847
## class 2: 0.1976 0.6952 0.1072
## class 3: 0.4813 0.2773 0.2414
##
## $I.laugh.a.lot
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.1848 0.8152
## class 2: 0.0529 0.6622 0.2849
## class 3: 0.3100 0.5137 0.1763
##
## $I.am.well.satisfied.about.everything.in.my.life
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0456 0.4154 0.5391
## class 2: 0.1441 0.6782 0.1777
## class 3: 0.6850 0.2784 0.0366
##
## $I.don.t.think.I.look.attractive
## Pr(1) Pr(2) Pr(3)
## class 1: 0.2001 0.1540 0.6460
## class 2: 0.1957 0.5888 0.2156
## class 3: 0.3795 0.3117 0.3087
##
## $There.is.a.gap.between.what.I.would.like.to.do.and.what.I.have.done
## Pr(1) Pr(2) Pr(3)
## class 1: 0.3081 0.5534 0.1385
## class 2: 0.3736 0.6083 0.0180
## class 3: 0.6550 0.2422 0.1028
##
## $I.am.very.happy
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.2462 0.7538
## class 2: 0.0000 0.8561 0.1439
## class 3: 0.7198 0.2113 0.0690
##
## $I.find.beauty.in.some.things
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0152 0.0615 0.9233
## class 2: 0.0000 0.5545 0.4455
## class 3: 0.1032 0.4461 0.4507
##
## $I.always.have.a.cheerful.effect.on.others
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.1232 0.8768
## class 2: 0.0532 0.7505 0.1963
## class 3: 0.3436 0.4474 0.2090
##
## $I.can.fit.in..find.time.for..everything.I.want.to
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0154 0.4454 0.5392
## class 2: 0.1773 0.6087 0.2141
## class 3: 0.5515 0.3104 0.1381
##
## $I.feel.that.I.am.not.especially.in.control.of.my.life
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1231 0.2303 0.6467
## class 2: 0.0703 0.7678 0.1618
## class 3: 0.4826 0.3129 0.2045
##
## $I.feel.able.to.take.anything.on
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0617 0.1990 0.7393
## class 2: 0.0533 0.7147 0.2320
## class 3: 0.5832 0.3129 0.1039
##
## $I.feel.fully.mentally.alert
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0308 0.1700 0.7992
## class 2: 0.0694 0.6792 0.2514
## class 3: 0.4154 0.4798 0.1048
##
## $I.often.experience.joy.and.elation
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0306 0.2142 0.7552
## class 2: 0.0352 0.7509 0.2139
## class 3: 0.4128 0.4840 0.1032
##
## $I.don.t.find.it.easy.to.make.decisions
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1988 0.2471 0.5541
## class 2: 0.2147 0.6242 0.1611
## class 3: 0.3458 0.4136 0.2406
##
## $I.don.t.have.a.particular.sense.of.meaning.and.purpose.in.my.life
## Pr(1) Pr(2) Pr(3)
## class 1: 0.2002 0.0757 0.7241
## class 2: 0.0903 0.6090 0.3007
## class 3: 0.4097 0.3786 0.2117
##
## $I.feel.I.have.a.great.deal.of.energy
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0776 0.2312 0.6912
## class 2: 0.1426 0.7309 0.1265
## class 3: 0.6508 0.2104 0.1388
##
## $I.usually.have.a.good.influence.on.events
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0154 0.1377 0.8469
## class 2: 0.0000 0.8219 0.1781
## class 3: 0.3427 0.5182 0.1390
##
## $I.don.t.have.fun.with.other.people
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1371 0.0616 0.8013
## class 2: 0.0726 0.6430 0.2844
## class 3: 0.1384 0.5504 0.3112
##
## $I.don.t.feel.particularly.healthy
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1533 0.1232 0.7235
## class 2: 0.2125 0.5370 0.2505
## class 3: 0.2116 0.6506 0.1379
##
## $I.don.t.have.particularly.happy.memories.of.the.past
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1371 0.1232 0.7396
## class 2: 0.1977 0.4805 0.3218
## class 3: 0.1386 0.4847 0.3766
##
## Estimated class population shares
## 0.4329 0.3726 0.1945
##
## Predicted class memberships (by modal posterior prob.)
## 0.4333 0.3733 0.1933
##
## =========================================================
## Fit for 3 latent classes:
## =========================================================
## number of observations: 150
## number of estimated parameters: 176
## residual degrees of freedom: -26
## maximum log-likelihood: -3638.342
##
## AIC(3): 7628.683
## BIC(3): 8158.555
## G^2(3): 5776.265 (Likelihood ratio/deviance statistic)
## X^2(3): 8.059502e+15 (Chi-square goodness of fit)
##
## ALERT: number of parameters estimated ( 176 ) exceeds number of observations ( 150 )
##
## ALERT: negative degrees of freedom; respecify model
## 
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(ohq_mclust_cluster) 4 1.66 0.4160 0.69 0.6
## Residuals 145 87.46 0.6032
## 29 observations deleted due to missingness## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = OHQ_Score ~ factor(ohq_mclust_cluster), data = telus)
##
## $`factor(ohq_mclust_cluster)`
## diff lwr upr p adj
## 2-1 0.070513478 -0.6219090 0.7629360 0.9986150
## 3-1 0.088022259 -0.4542526 0.6302971 0.9915618
## 4-1 -0.459222770 -1.4384560 0.5200105 0.6944312
## 5-1 -0.003433587 -0.5228482 0.5159811 1.0000000
## 3-2 0.017508780 -0.6069524 0.6419700 0.9999918
## 4-2 -0.529736248 -1.5567648 0.4972923 0.6127173
## 5-2 -0.073947066 -0.6786630 0.5307689 0.9971679
## 4-3 -0.547245028 -1.4796616 0.3851716 0.4863009
## 5-3 -0.091455846 -0.5160668 0.3331551 0.9756491
## 5-4 0.455789182 -0.4635205 1.3750989 0.6480536## ----------------------------------------------------
## Gaussian finite mixture model fitted by EM algorithm
## ----------------------------------------------------
##
## Mclust VII (spherical, varying volume) model with 4 components:
##
## log-likelihood n df BIC ICL
## -4326.608 163 95 -9137.122 -9145.295
##
## Clustering table:
## 1 2 3 4
## 76 72 10 5
## Conditional item response (column) probabilities,
## by outcome variable, for each class (row)
##
## $How.would.you.rate.your.quality.of.life.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0281 0.9719
## class 2: 0.0000 0.1973 0.8027
## class 3: 0.3306 0.4979 0.1715
##
## $How.satisfied.are.you.with.your.health.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0679 0.0932 0.8388
## class 2: 0.1482 0.3820 0.4698
## class 3: 0.6047 0.1704 0.2249
##
## $To.what.extent.do.you.feel.that.physical.pain.prevents.you.from.doing.what.you.need.to.do.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.2339 0.1003 0.6658
## class 2: 0.2765 0.2483 0.4752
## class 3: 0.3582 0.1404 0.5014
##
## $How.much.do.you.need.any.medical.treatment.to.function.in.your.daily.life.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1846 0.0950 0.7204
## class 2: 0.1410 0.3118 0.5472
## class 3: 0.2763 0.1914 0.5323
##
## $How.much.do.you.enjoy.life.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0567 0.9433
## class 2: 0.0000 0.6098 0.3902
## class 3: 0.6062 0.2802 0.1136
##
## $To.what.extent.do.you.feel.your.life.to.be.meaningful.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0554 0.9446
## class 2: 0.0538 0.5764 0.3699
## class 3: 0.5516 0.2236 0.2248
##
## $How.well.are.you.able.to.concentrate.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.1469 0.8531
## class 2: 0.1240 0.4402 0.4358
## class 3: 0.3342 0.5272 0.1387
##
## $How.safe.do.you.feel.in.your.daily.life.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0434 0.9566
## class 2: 0.0533 0.5017 0.4450
## class 3: 0.3043 0.3335 0.3621
##
## $How.healthy.is.your.physical.environment.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0304 0.9696
## class 2: 0.0543 0.5539 0.3917
## class 3: 0.4681 0.3346 0.1973
##
## $Do.you.have.enough.energy.for.everyday.life.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.1005 0.8995
## class 2: 0.1590 0.4287 0.4123
## class 3: 0.6665 0.2226 0.1108
##
## $Are.you.able.to.accept.your.bodily.appearance.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0432 0.1505 0.8063
## class 2: 0.2138 0.4709 0.3153
## class 3: 0.6358 0.2253 0.1389
##
## $Have.you.enough.money.to.meet.your.needs.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1272 0.2144 0.6584
## class 2: 0.3394 0.3543 0.3062
## class 3: 0.5544 0.2778 0.1678
##
## $How.available.to.you.is.the.information.that.you.need.in.your.day.to.day.life.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0686 0.9314
## class 2: 0.0696 0.3997 0.5307
## class 3: 0.3622 0.2195 0.4183
##
## $To.what.extent.do.you.have.the.opportunity.for.leisure.activities
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0766 0.1679 0.7555
## class 2: 0.1507 0.5235 0.3258
## class 3: 0.5287 0.3312 0.1401
##
## $How.well.are.you.able.to.get.around.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0401 0.9599
## class 2: 0.0718 0.2875 0.6407
## class 3: 0.2210 0.2538 0.5252
##
## $How.satisfied.are.you.with.your.sleep.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1215 0.1146 0.7640
## class 2: 0.3283 0.3021 0.3695
## class 3: 0.6928 0.1127 0.1944
##
## $How.satisfied.are.you.with.your.ability.to.perform.your.daily.living.activities.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0133 0.0359 0.9508
## class 2: 0.0365 0.4359 0.5275
## class 3: 0.5794 0.2282 0.1924
##
## $How.satisfied.are.you.with.your.capacity.for.work.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0133 0.0936 0.8931
## class 2: 0.1608 0.3279 0.5112
## class 3: 0.4723 0.1696 0.3581
##
## $How.satisfied.are.you.with.yourself.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0000 0.0417 0.9583
## class 2: 0.1233 0.3432 0.5335
## class 3: 0.6658 0.2207 0.1134
##
## $How.satisfied.are.you.with.your.personal.relationships.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0315 0.0586 0.9100
## class 2: 0.3531 0.3908 0.2562
## class 3: 0.5839 0.1150 0.3011
##
## $How.satisfied.are.you.with.your.sex.life.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.1116 0.1287 0.7597
## class 2: 0.2724 0.4602 0.2674
## class 3: 0.6324 0.0916 0.2761
##
## $How.satisfied.are.you.with.the.support.you.get.from.your.friends.
## Pr(1) Pr(2) Pr(3)
## class 1: 0.0294 0.0575 0.9131
## class 2: 0.1231 0.5167 0.3602
## class 3: 0.4983 0.2554 0.2464
##
## Estimated class population shares
## 0.4374 0.3399 0.2227
##
## Predicted class memberships (by modal posterior prob.)
## 0.4417 0.3374 0.2209
##
## =========================================================
## Fit for 3 latent classes:
## =========================================================
## number of observations: 163
## number of estimated parameters: 134
## residual degrees of freedom: 29
## maximum log-likelihood: -2740.551
##
## AIC(3): 5749.101
## BIC(3): 6163.664
## G^2(3): 3911.686 (Likelihood ratio/deviance statistic)
## X^2(3): 37760400525 (Chi-square goodness of fit)
## 
## Df Sum Sq Mean Sq F value Pr(>F)
## factor(whoqol_mclust_cluster) 3 4.42 1.4722 3.763 0.0121 *
## Residuals 159 62.21 0.3912
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 16 observations deleted due to missingness## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = WHOQOL_Score ~ factor(whoqol_mclust_cluster), data = telus)
##
## $`factor(whoqol_mclust_cluster)`
## diff lwr upr p adj
## 2-1 -0.001298876 -0.2683828 0.2657850 0.9999993
## 3-1 0.039889796 -0.5064116 0.5861912 0.9975745
## 4-1 -0.951019295 -1.7008109 -0.2012277 0.0066353
## 3-2 0.041188672 -0.5068744 0.5892518 0.9973576
## 4-2 -0.949720418 -1.7007966 -0.1986442 0.0068544
## 4-3 -0.990909091 -1.8804184 -0.1013998 0.0224092With other vars
Categorical Analysis
Injury
##
## Frequency Table:
##
## No (0)
## 3 131
## Yes, own opinion (2) Yes, physician's diagnosis (1)
## 16 29
##
## Proportions:
##
## No (0)
## 0.01675978 0.73184358
## Yes, own opinion (2) Yes, physician's diagnosis (1)
## 0.08938547 0.16201117
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean
## 1 3.6909091
## 2 No (0) 3.5748486
## 3 Yes, own opinion (2) 3.7625812
## 4 Yes, physician's diagnosis (1) 3.2372742
## data[[continuous_var]].sd data[[continuous_var]].n
## 1 0.3453349 3.0000000
## 2 0.6461397 131.0000000
## 3 0.5161290 16.0000000
## 4 0.6668480 29.0000000
##
## ANOVA Results:
## Df Sum Sq Mean Sq F value Pr(>F)
## Injury.due.to.an.accident 3 3.68 1.2258 3.023 0.0311 *
## Residuals 175 70.96 0.4055
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $Injury.due.to.an.accident
## diff lwr
## No (0)- -0.11606045 -1.0805452
## Yes, own opinion (2)- 0.07167208 -0.9675191
## Yes, physician's diagnosis (1)- -0.45363487 -1.4553740
## Yes, own opinion (2)-No (0) 0.18773253 -0.2496911
## Yes, physician's diagnosis (1)-No (0) -0.33757442 -0.6765470
## Yes, physician's diagnosis (1)-Yes, own opinion (2) -0.52530695 -1.0396899
## upr p adj
## No (0)- 0.848424339 0.9894292
## Yes, own opinion (2)- 1.110863229 0.9979598
## Yes, physician's diagnosis (1)- 0.548104219 0.6436492
## Yes, own opinion (2)-No (0) 0.625156204 0.6818760
## Yes, physician's diagnosis (1)-No (0) 0.001398185 0.0513937
## Yes, physician's diagnosis (1)-Yes, own opinion (2) -0.010924046 0.0433217
##
## Frequency Table:
##
## No (0)
## 3 131
## Yes, own opinion (2) Yes, physician's diagnosis (1)
## 16 29
##
## Proportions:
##
## No (0)
## 0.01675978 0.73184358
## Yes, own opinion (2) Yes, physician's diagnosis (1)
## 0.08938547 0.16201117
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean
## 1 3.8349754
## 2 No (0) 3.6624859
## 3 Yes, own opinion (2) 3.5650657
## 4 Yes, physician's diagnosis (1) 3.5349074
## data[[continuous_var]].sd data[[continuous_var]].n
## 1 0.1959173 3.0000000
## 2 0.8118286 131.0000000
## 3 0.4404682 16.0000000
## 4 0.7426828 29.0000000
##
## ANOVA Results:
## Df Sum Sq Mean Sq F value Pr(>F)
## Injury.due.to.an.accident 3 0.59 0.1958 0.329 0.804
## Residuals 175 104.11 0.5949
Missed work
##
## Frequency Table:
##
## None (5) Max. 9 days (4) 10-24 days (3)
## 0 53 70 28
## 25-99 days (2) 100-354 days (1)
## 10 18
##
## Proportions:
##
## None (5) Max. 9 days (4) 10-24 days (3)
## 0.00000000 0.29608939 0.39106145 0.15642458
## 25-99 days (2) 100-354 days (1)
## 0.05586592 0.10055866
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 None (5) 3.6989259 0.6320818
## 2 Max. 9 days (4) 3.6426311 0.5270830
## 3 10-24 days (3) 3.4119511 0.6045522
## 4 25-99 days (2) 3.1900433 0.6595273
## 5 100-354 days (1) 3.0554353 0.8719157
## data[[continuous_var]].n
## 1 53.0000000
## 2 70.0000000
## 3 28.0000000
## 4 10.0000000
## 5 18.0000000
##
## ANOVA Results:
## Df
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 4
## Residuals 174
## Sum Sq
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 7.99
## Residuals 66.65
## Mean Sq
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 1.9965
## Residuals 0.3831
## F value
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 5.212
## Residuals
## Pr(>F)
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 0.000546
## Residuals
##
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness
## diff lwr upr p adj
## Max. 9 days (4)-None (5) -0.05629483 -0.3669433 0.25435362 0.9873170
## 10-24 days (3)-None (5) -0.28697477 -0.6855673 0.11161775 0.2778167
## 25-99 days (2)-None (5) -0.50888263 -1.0970974 0.07933215 0.1243668
## 100-354 days (1)-None (5) -0.64349061 -1.1089251 -0.17805611 0.0017754
## 10-24 days (3)-Max. 9 days (4) -0.23067995 -0.6121747 0.15081482 0.4571804
## 25-99 days (2)-Max. 9 days (4) -0.45258780 -1.0293537 0.12417808 0.1987778
## 100-354 days (1)-Max. 9 days (4) -0.58719579 -1.0380744 -0.13631716 0.0038890
## 25-99 days (2)-10-24 days (3) -0.22190785 -0.8504239 0.40660819 0.8669060
## 100-354 days (1)-10-24 days (3) -0.35651584 -0.8719429 0.15891125 0.3176446
## 100-354 days (1)-25-99 days (2) -0.13460798 -0.8075015 0.53828554 0.9816402
##
## Frequency Table:
##
## None (5) Max. 9 days (4) 10-24 days (3)
## 0 53 70 28
## 25-99 days (2) 100-354 days (1)
## 10 18
##
## Proportions:
##
## None (5) Max. 9 days (4) 10-24 days (3)
## 0.00000000 0.29608939 0.39106145 0.15642458
## 25-99 days (2) 100-354 days (1)
## 0.05586592 0.10055866
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 None (5) 3.8434739 0.8121410
## 2 Max. 9 days (4) 3.6883628 0.7274560
## 3 10-24 days (3) 3.4724666 0.7377717
## 4 25-99 days (2) 3.2701970 0.6756515
## 5 100-354 days (1) 3.2790777 0.6956572
## data[[continuous_var]].n
## 1 53.0000000
## 2 70.0000000
## 3 28.0000000
## 4 10.0000000
## 5 18.0000000
##
## ANOVA Results:
## Df
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 4
## Residuals 174
## Sum Sq
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 6.85
## Residuals 97.84
## Mean Sq
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 1.7133
## Residuals 0.5623
## F value
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 3.047
## Residuals
## Pr(>F)
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness 0.0185
## Residuals
##
## During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness *
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $During.the.last.12.months..how.many.whole.days.have.you.been.off.work.because.of.illness
## diff lwr upr
## Max. 9 days (4)-None (5) -0.155111067 -0.5314946 0.2212724410
## 10-24 days (3)-None (5) -0.371007280 -0.8539444 0.1119297912
## 25-99 days (2)-None (5) -0.573276809 -1.2859613 0.1394077103
## 100-354 days (1)-None (5) -0.564396130 -1.1283193 -0.0004729167
## 10-24 days (3)-Max. 9 days (4) -0.215896213 -0.6781175 0.2463251219
## 25-99 days (2)-Max. 9 days (4) -0.418165741 -1.1169787 0.2806472315
## 100-354 days (1)-Max. 9 days (4) -0.409285063 -0.9555723 0.1370021675
## 25-99 days (2)-10-24 days (3) -0.202269529 -0.9637833 0.5592442538
## 100-354 days (1)-10-24 days (3) -0.193388850 -0.8178834 0.4311056718
## 100-354 days (1)-25-99 days (2) 0.008880679 -0.8064011 0.8241624804
## p adj
## Max. 9 days (4)-None (5) 0.7872052
## 10-24 days (3)-None (5) 0.2172455
## 25-99 days (2)-None (5) 0.1781669
## 100-354 days (1)-None (5) 0.0496942
## 10-24 days (3)-Max. 9 days (4) 0.6992218
## 25-99 days (2)-Max. 9 days (4) 0.4680205
## 100-354 days (1)-Max. 9 days (4) 0.2399736
## 25-99 days (2)-10-24 days (3) 0.9487248
## 100-354 days (1)-10-24 days (3) 0.9131823
## 100-354 days (1)-25-99 days (2) 0.9999998
Mental Demands
##
## Frequency Table:
##
## Very poor (1) Rather poor (2) Moderate (3) Rather good (4)
## 2 4 15 55 66
## Very good (5)
## 37
##
## Proportions:
##
## Very poor (1) Rather poor (2) Moderate (3) Rather good (4)
## 0.01117318 0.02234637 0.08379888 0.30726257 0.36871508
## Very good (5)
## 0.20670391
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 3.76363636 0.05142595
## 2 Very poor (1) 2.76136364 0.77794584
## 3 Rather poor (2) 2.95708514 0.57317330
## 4 Moderate (3) 3.24463162 0.50819453
## 5 Rather good (4) 3.62027089 0.54308597
## 6 Very good (5) 4.13887914 0.49963821
## data[[continuous_var]].n
## 1 2.00000000
## 2 4.00000000
## 3 15.00000000
## 4 55.00000000
## 5 66.00000000
## 6 37.00000000
##
## ANOVA Results:
## Df
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 5
## Residuals 173
## Sum Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 26.12
## Residuals 48.52
## Mean Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 5.223
## Residuals 0.280
## F value
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 18.62
## Residuals
## Pr(>F)
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 8.79e-15
## Residuals
##
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.
## diff lwr upr p adj
## Very poor (1)- -1.0022727 -2.32405609 0.3195106 0.2499754
## Rather poor (2)- -0.8065512 -1.95548099 0.3423785 0.3335548
## Moderate (3)- -0.5190047 -1.61768352 0.5796740 0.7499224
## Rather good (4)- -0.1433655 -1.23882702 0.9520961 0.9989929
## Very good (5)- 0.3752428 -0.73277338 1.4832589 0.9249992
## Rather poor (2)-Very poor (1) 0.1957215 -0.66315520 1.0545982 0.9862838
## Moderate (3)-Very poor (1) 0.4832680 -0.30712725 1.2736632 0.4929850
## Rather good (4)-Very poor (1) 0.8589073 0.07299021 1.6448243 0.0232899
## Very good (5)-Very poor (1) 1.3775155 0.57419155 2.1808395 0.0000266
## Moderate (3)-Rather poor (2) 0.2875465 -0.15703533 0.7321283 0.4279287
## Rather good (4)-Rather poor (2) 0.6631858 0.22661508 1.0997564 0.0002953
## Very good (5)-Rather poor (2) 1.1817940 0.71461337 1.6489746 0.0000000
## Rather good (4)-Moderate (3) 0.3756393 0.09698287 0.6542957 0.0019923
## Very good (5)-Moderate (3) 0.8942475 0.56972760 1.2187674 0.0000000
## Very good (5)-Rather good (4) 0.5186082 0.20515308 0.8320634 0.0000572
##
## Frequency Table:
##
## Very poor (1) Rather poor (2) Moderate (3) Rather good (4)
## 2 4 15 55 66
## Very good (5)
## 37
##
## Proportions:
##
## Very poor (1) Rather poor (2) Moderate (3) Rather good (4)
## 0.01117318 0.02234637 0.08379888 0.30726257 0.36871508
## Very good (5)
## 0.20670391
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 3.3965517 0.2194469
## 2 Very poor (1) 3.2500000 0.7702222
## 3 Rather poor (2) 2.9279146 0.5922612
## 4 Moderate (3) 3.4100791 0.6680964
## 5 Rather good (4) 3.6813980 0.6904483
## 6 Very good (5) 4.2325811 0.7173528
## data[[continuous_var]].n
## 1 2.0000000
## 2 4.0000000
## 3 15.0000000
## 4 55.0000000
## 5 66.0000000
## 6 37.0000000
##
## ANOVA Results:
## Df
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 5
## Residuals 173
## Sum Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 24.34
## Residuals 80.35
## Mean Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 4.869
## Residuals 0.464
## F value
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 10.48
## Residuals
## Pr(>F)
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. 8.39e-09
## Residuals
##
## How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work. ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $How.do.you.rate.your.current.work.ability.with.respect.to.the.mental.demands.of.your.work.
## diff lwr upr p adj
## Very poor (1)- -0.14655172 -1.84751310 1.5544097 0.9998696
## Rather poor (2)- -0.46863711 -1.94715864 1.0098844 0.9426430
## Moderate (3)- 0.01352739 -1.40032772 1.4273825 1.0000000
## Rather good (4)- 0.28484627 -1.12486870 1.6945612 0.9920879
## Very good (5)- 0.83602938 -0.58984171 2.2619005 0.5404175
## Rather poor (2)-Very poor (1) -0.32208539 -1.42734677 0.7831760 0.9596158
## Moderate (3)-Very poor (1) 0.16007912 -0.85705562 1.1772139 0.9975561
## Rather good (4)-Very poor (1) 0.43139800 -0.57997389 1.4427699 0.8219620
## Very good (5)-Very poor (1) 0.98258110 -0.05119120 2.0163534 0.0728130
## Moderate (3)-Rather poor (2) 0.48216450 -0.08995383 1.0542828 0.1521648
## Rather good (4)-Rather poor (2) 0.75348338 0.19167433 1.3152924 0.0021421
## Very good (5)-Rather poor (2) 1.30466649 0.70346645 1.9058665 0.0000000
## Rather good (4)-Moderate (3) 0.27131888 -0.08727526 0.6299130 0.2522281
## Very good (5)-Moderate (3) 0.82250199 0.40488753 1.2401164 0.0000008
## Very good (5)-Rather good (4) 0.55118311 0.14780753 0.9545587 0.0016380
Physical Demands
##
## Frequency Table:
##
## Very poor (1) Rather poor (2) Moderate (3) Rather good (4)
## 0 3 11 50 72
## Very good (5)
## 43
##
## Proportions:
##
## Very poor (1) Rather poor (2) Moderate (3) Rather good (4)
## 0.00000000 0.01675978 0.06145251 0.27932961 0.40223464
## Very good (5)
## 0.24022346
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 Very poor (1) 2.6363636 0.7925271
## 2 Rather poor (2) 2.7512790 0.4440158
## 3 Moderate (3) 3.2852679 0.5714570
## 4 Rather good (4) 3.6161195 0.4511017
## 5 Very good (5) 3.9689067 0.6917212
## data[[continuous_var]].n
## 1 3.0000000
## 2 11.0000000
## 3 50.0000000
## 4 72.0000000
## 5 43.0000000
##
## ANOVA Results:
## Df
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 4
## Residuals 174
## Sum Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 20.86
## Residuals 53.77
## Mean Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 5.216
## Residuals 0.309
## F value
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 16.88
## Residuals
## Pr(>F)
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 1.04e-11
## Residuals
##
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.
## diff lwr upr p adj
## Rather poor (2)-Very poor (1) 0.1149154 -0.88321858 1.1130494 0.9977838
## Moderate (3)-Very poor (1) 0.6489043 -0.26200281 1.5598114 0.2882135
## Rather good (4)-Very poor (1) 0.9797559 0.07676048 1.8827513 0.0261050
## Very good (5)-Very poor (1) 1.3325430 0.41744874 2.2476373 0.0008342
## Moderate (3)-Rather poor (2) 0.5339889 0.02364216 1.0443357 0.0353332
## Rather good (4)-Rather poor (2) 0.8648405 0.36875305 1.3609280 0.0000325
## Very good (5)-Rather poor (2) 1.2176277 0.69984428 1.7354110 0.0000000
## Rather good (4)-Moderate (3) 0.3308516 0.04874680 0.6129564 0.0125971
## Very good (5)-Moderate (3) 0.6836387 0.36492261 1.0023549 0.0000002
## Very good (5)-Rather good (4) 0.3527871 0.05744173 0.6481326 0.0104309
##
## Frequency Table:
##
## Very poor (1) Rather poor (2) Moderate (3) Rather good (4)
## 0 3 11 50 72
## Very good (5)
## 43
##
## Proportions:
##
## Very poor (1) Rather poor (2) Moderate (3) Rather good (4)
## 0.00000000 0.01675978 0.06145251 0.27932961 0.40223464
## Very good (5)
## 0.24022346
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 Very poor (1) 3.4022989 1.0001982
## 2 Rather poor (2) 3.0509404 0.9485929
## 3 Moderate (3) 3.4405419 0.6508604
## 4 Rather good (4) 3.6914959 0.6407954
## 5 Very good (5) 3.9363234 0.8977151
## data[[continuous_var]].n
## 1 3.0000000
## 2 11.0000000
## 3 50.0000000
## 4 72.0000000
## 5 43.0000000
##
## ANOVA Results:
## Df
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 4
## Residuals 174
## Sum Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 9.94
## Residuals 94.76
## Mean Sq
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 2.4848
## Residuals 0.5446
## F value
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 4.563
## Residuals
## Pr(>F)
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. 0.00158
## Residuals
##
## How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work. **
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $How.do.you.rate.your.current.work.ability.with.respect.to.the.physical.demands.of.your.work.
## diff lwr upr p adj
## Rather poor (2)-Very poor (1) -0.35135841 -1.67634901 0.9736322 0.9490204
## Moderate (3)-Very poor (1) 0.03824302 -1.17095675 1.2474428 0.9999868
## Rather good (4)-Very poor (1) 0.28919702 -0.90950022 1.4878943 0.9635652
## Very good (5)-Very poor (1) 0.53402452 -0.68073360 1.7487826 0.7445327
## Moderate (3)-Rather poor (2) 0.38960143 -0.28786740 1.0670703 0.5088343
## Rather good (4)-Rather poor (2) 0.64055544 -0.01798463 1.2990955 0.0608670
## Very good (5)-Rather poor (2) 0.88538293 0.19804223 1.5727236 0.0044489
## Rather good (4)-Moderate (3) 0.25095400 -0.12353099 0.6254390 0.3500086
## Very good (5)-Moderate (3) 0.49578149 0.07269614 0.9188669 0.0127032
## Very good (5)-Rather good (4) 0.24482749 -0.14723401 0.6368890 0.4235680
Ability to continue
##
## Frequency Table:
##
## Unlikely (1) Not Certain (4)
## 3 13 39
## Relatively certain (7)
## 124
##
## Proportions:
##
## Unlikely (1) Not Certain (4)
## 0.01675978 0.07262570 0.21787709
## Relatively certain (7)
## 0.69273743
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 3.4696970 0.3812044
## 2 Unlikely (1) 2.9860140 0.8199444
## 3 Not Certain (4) 3.1134976 0.5372107
## 4 Relatively certain (7) 3.7323102 0.5676092
## data[[continuous_var]].n
## 1 3.0000000
## 2 13.0000000
## 3 39.0000000
## 4 124.0000000
##
## ANOVA Results:
## Df
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 3
## Residuals 175
## Sum Sq
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 15.68
## Residuals 58.95
## Mean Sq
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 5.228
## Residuals 0.337
## F value
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 15.52
## Residuals
## Pr(>F)
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 5.38e-09
## Residuals
##
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.
## diff lwr upr
## Unlikely (1)- -0.4836830 -1.4479853 0.4806193
## Not Certain (4)- -0.3561994 -1.2582216 0.5458229
## Relatively certain (7)- 0.2626133 -0.6170489 1.1422754
## Not Certain (4)-Unlikely (1) 0.1274836 -0.3546675 0.6096348
## Relatively certain (7)-Unlikely (1) 0.7462963 0.3073986 1.1851939
## Relatively certain (7)-Not Certain (4) 0.6188126 0.3424138 0.8952115
## p adj
## Unlikely (1)- 0.5634847
## Not Certain (4)- 0.7354462
## Relatively certain (7)- 0.8659511
## Not Certain (4)-Unlikely (1) 0.9023890
## Relatively certain (7)-Unlikely (1) 0.0001051
## Relatively certain (7)-Not Certain (4) 0.0000002
##
## Frequency Table:
##
## Unlikely (1) Not Certain (4)
## 3 13 39
## Relatively certain (7)
## 124
##
## Proportions:
##
## Unlikely (1) Not Certain (4)
## 0.01675978 0.07262570 0.21787709
## Relatively certain (7)
## 0.69273743
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 3.7454844 0.6964014
## 2 Unlikely (1) 3.2572944 0.8335056
## 3 Not Certain (4) 3.1838870 0.5953913
## 4 Relatively certain (7) 3.8152506 0.7438823
## data[[continuous_var]].n
## 1 3.0000000
## 2 13.0000000
## 3 39.0000000
## 4 124.0000000
##
## ANOVA Results:
## Df
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 3
## Residuals 175
## Sum Sq
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 13.86
## Residuals 90.84
## Mean Sq
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 4.619
## Residuals 0.519
## F value
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 8.898
## Residuals
## Pr(>F)
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. 1.61e-05
## Residuals
##
## Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now. ***
## Residuals
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $Do.you.believe..according.to.your.present.state.of.health..that.you.will.be.able.to.do.your.current.job.two.years.from.now.
## diff lwr upr
## Unlikely (1)- -0.48818997 -1.68520655 0.7088266
## Not Certain (4)- -0.56159741 -1.68130389 0.5581091
## Relatively certain (7)- 0.06976616 -1.02218413 1.1617164
## Not Certain (4)-Unlikely (1) -0.07340744 -0.67191573 0.5251009
## Relatively certain (7)-Unlikely (1) 0.55795613 0.01313964 1.1027726
## Relatively certain (7)-Not Certain (4) 0.63136356 0.28826165 0.9744655
## p adj
## Unlikely (1)- 0.7155241
## Not Certain (4)- 0.5635356
## Relatively certain (7)- 0.9983752
## Not Certain (4)-Unlikely (1) 0.9888229
## Relatively certain (7)-Unlikely (1) 0.0424776
## Relatively certain (7)-Not Certain (4) 0.0000225
Satisfaction
Transport
##
## Frequency Table:
##
## 1 2 3 4 5
## 0 13 16 34 73 43
##
## Proportions:
##
## 1 2 3 4 5
## 0.00000000 0.07262570 0.08938547 0.18994413 0.40782123 0.24022346
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 1 2.9532135 0.5392171
## 2 2 2.8835227 0.5326706
## 3 3 3.2007194 0.5380363
## 4 4 3.6775096 0.4865633
## 5 5 3.9918454 0.5946084
## data[[continuous_var]].n
## 1 13.0000000
## 2 16.0000000
## 3 34.0000000
## 4 73.0000000
## 5 43.0000000
##
## ANOVA Results:
## Df Sum Sq Mean Sq F value Pr(>F)
## transport_satisfaction 4 25.44 6.361 22.5 5.43e-15 ***
## Residuals 174 49.19 0.283
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $transport_satisfaction
## diff lwr upr p adj
## 2-1 -0.06969073 -0.61698094 0.4775995 0.9967187
## 3-1 0.24750593 -0.23045032 0.7254622 0.6108181
## 4-1 0.72429617 0.28306471 1.1655276 0.0001081
## 5-1 1.03863191 0.57471686 1.5025470 0.0000000
## 3-2 0.31719665 -0.12716431 0.7615576 0.2862051
## 4-2 0.79398689 0.38938871 1.1985851 0.0000020
## 5-2 1.10832264 0.67910048 1.5375448 0.0000000
## 4-3 0.47679024 0.17246264 0.7811178 0.0002525
## 5-3 0.79112599 0.45475229 1.1274997 0.0000000
## 5-4 0.31433574 0.03257292 0.5960986 0.0203817
##
## Frequency Table:
##
## 1 2 3 4 5
## 0 13 16 34 73 43
##
## Proportions:
##
## 1 2 3 4 5
## 0.00000000 0.07262570 0.08938547 0.18994413 0.40782123 0.24022346
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 1 3.2058355 0.6835468
## 2 2 3.0072352 0.6879039
## 3 3 3.3803366 0.6966984
## 4 4 3.6967363 0.6890647
## 5 5 4.0990494 0.7128998
## data[[continuous_var]].n
## 1 13.0000000
## 2 16.0000000
## 3 34.0000000
## 4 73.0000000
## 5 43.0000000
##
## ANOVA Results:
## Df Sum Sq Mean Sq F value Pr(>F)
## transport_satisfaction 4 20.44 5.111 10.55 1.12e-07 ***
## Residuals 174 84.25 0.484
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $transport_satisfaction
## diff lwr upr p adj
## 2-1 -0.1986003 -0.91484677 0.5176461 0.9404283
## 3-1 0.1745011 -0.45100704 0.8000092 0.9391564
## 4-1 0.4909008 -0.08654510 1.0683466 0.1363299
## 5-1 0.8932138 0.28608162 1.5003460 0.0007121
## 3-2 0.3731014 -0.20844009 0.9546429 0.3952847
## 4-2 0.6895011 0.15999770 1.2190045 0.0038954
## 5-2 1.0918141 0.53008502 1.6535433 0.0000026
## 4-3 0.3163997 -0.08187817 0.7146775 0.1884044
## 5-3 0.7187127 0.27849572 1.1589298 0.0001198
## 5-4 0.4023131 0.03356607 0.7710601 0.0248780
Access to Health Services
##
## Frequency Table:
##
## 1 2 3 4 5
## 0 7 26 37 82 27
##
## Proportions:
##
## 1 2 3 4 5
## 0.00000000 0.03910615 0.14525140 0.20670391 0.45810056 0.15083799
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 1 2.6883117 0.4926656
## 2 2 3.2119547 0.7029207
## 3 3 3.2184743 0.4666464
## 4 4 3.6657689 0.5396854
## 5 5 4.1279461 0.5213397
## data[[continuous_var]].n
## 1 7.0000000
## 2 26.0000000
## 3 37.0000000
## 4 82.0000000
## 5 27.0000000
##
## ANOVA Results:
## Df Sum Sq Mean Sq F value Pr(>F)
## health_access_satisfaction 4 22.33 5.583 18.57 9.98e-13 ***
## Residuals 174 52.31 0.301
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $health_access_satisfaction
## diff lwr upr p adj
## 2-1 0.523643024 -0.11993123 1.1672173 0.1690253
## 3-1 0.530162630 -0.09278831 1.1531136 0.1355671
## 4-1 0.977457224 0.38232092 1.5725935 0.0001070
## 5-1 1.439634440 0.79859324 2.0806756 0.0000000
## 3-2 0.006519607 -0.38025694 0.3932961 0.9999989
## 4-2 0.453814200 0.11364447 0.7939839 0.0028658
## 5-2 0.915991416 0.50070592 1.3312769 0.0000001
## 4-3 0.447294594 0.14796953 0.7466197 0.0005566
## 5-3 0.909471809 0.52692496 1.2920187 0.0000000
## 5-4 0.462177216 0.12682450 0.7975299 0.0018550
##
## Frequency Table:
##
## 1 2 3 4 5
## 0 7 26 37 82 27
##
## Proportions:
##
## 1 2 3 4 5
## 0.00000000 0.03910615 0.14525140 0.20670391 0.45810056 0.15083799
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 1 2.6520056 0.6040808
## 2 2 3.3978780 0.7818531
## 3 3 3.4282497 0.6582597
## 4 4 3.6878472 0.7063236
## 5 5 4.2476434 0.6517962
## data[[continuous_var]].n
## 1 7.0000000
## 2 26.0000000
## 3 37.0000000
## 4 82.0000000
## 5 27.0000000
##
## ANOVA Results:
## Df Sum Sq Mean Sq F value Pr(>F)
## health_access_satisfaction 4 20.17 5.043 10.38 1.46e-07 ***
## Residuals 174 84.53 0.486
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $health_access_satisfaction
## diff lwr upr p adj
## 2-1 0.74587235 -0.07224790 1.5639926 0.0923787
## 3-1 0.77624405 -0.01565957 1.5681477 0.0576868
## 4-1 1.03584152 0.27929624 1.7923868 0.0020294
## 5-1 1.59563774 0.78073754 2.4105379 0.0000022
## 3-2 0.03037169 -0.46130385 0.5220472 0.9998100
## 4-2 0.28996917 -0.14245917 0.7223975 0.3493486
## 5-2 0.84976539 0.32184888 1.3776819 0.0001557
## 4-3 0.25959747 -0.12090857 0.6401035 0.3316225
## 5-3 0.81939370 0.33309499 1.3056924 0.0000651
## 5-4 0.55979622 0.13349134 0.9861011 0.0035080
Living Conditions Satisfaction
##
## Frequency Table:
##
## 1 2 3 4 5
## 0 11 9 33 80 42
##
## Proportions:
##
## 1 2 3 4 5
## 0.00000000 0.06285714 0.05142857 0.18857143 0.45714286 0.24000000
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 1 2.8925620 0.5761344
## 2 2 2.6758057 0.3808890
## 3 3 3.2621671 0.4539926
## 4 4 3.5099564 0.5478049
## 5 5 4.1701711 0.4681431
## data[[continuous_var]].n
## 1 11.0000000
## 2 9.0000000
## 3 33.0000000
## 4 80.0000000
## 5 42.0000000
##
## ANOVA Results:
## Df Sum Sq Mean Sq F value Pr(>F)
## living_conditions_satisfaction 4 30.63 7.658 29.74 <2e-16 ***
## Residuals 170 43.77 0.257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $living_conditions_satisfaction
## diff lwr upr p adj
## 2-1 -0.2167563 -0.84558420 0.4120716 0.8765790
## 3-1 0.3696051 -0.11748285 0.8566931 0.2282085
## 4-1 0.6173944 0.16749676 1.0672921 0.0019737
## 5-1 1.2776091 0.80374770 1.7514705 0.0000000
## 3-2 0.5863614 0.06024629 1.1124766 0.0205778
## 4-2 0.8341507 0.34226623 1.3260353 0.0000579
## 5-2 1.4943654 0.98047137 2.0082595 0.0000000
## 4-3 0.2477893 -0.04165944 0.5372380 0.1314332
## 5-3 0.9080040 0.58255462 1.2334533 0.0000000
## 5-4 0.6602147 0.39362417 0.9268052 0.0000000
##
## Frequency Table:
##
## 1 2 3 4 5
## 0 11 9 33 80 42
##
## Proportions:
##
## 1 2 3 4 5
## 0.00000000 0.06285714 0.05142857 0.18857143 0.45714286 0.24000000
##
## Summary Statistics:
## data[[categorical_var]] data[[continuous_var]].mean data[[continuous_var]].sd
## 1 1 3.2664577 0.8341822
## 2 2 2.9095512 0.6116287
## 3 3 3.3628153 0.6216988
## 4 4 3.5592530 0.7284168
## 5 5 4.2554659 0.5912257
## data[[continuous_var]].n
## 1 11.0000000
## 2 9.0000000
## 3 33.0000000
## 4 80.0000000
## 5 42.0000000
##
## ANOVA Results:
## Df Sum Sq Mean Sq F value Pr(>F)
## living_conditions_satisfaction 4 25.30 6.326 13.69 1.07e-09 ***
## Residuals 170 78.57 0.462
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 4 observations deleted due to missingness
##
## Post-hoc Pairwise Comparisons (Tukey HSD):
## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = as.formula(paste(continuous_var, "~", categorical_var)), data = data)
##
## $living_conditions_satisfaction
## diff lwr upr p adj
## 2-1 -0.35690650 -1.199417442 0.4856044 0.7695486
## 3-1 0.09635767 -0.556248502 0.7489638 0.9941793
## 4-1 0.29279531 -0.309982852 0.8955735 0.6670694
## 5-1 0.98900817 0.354123130 1.6238932 0.0002807
## 3-2 0.45326417 -0.251631056 1.1581594 0.3927413
## 4-2 0.64970181 -0.009330796 1.3087344 0.0554054
## 5-2 1.34591467 0.657393445 2.0344359 0.0000023
## 4-3 0.19643764 -0.191369146 0.5842444 0.6306567
## 5-3 0.89265051 0.456609669 1.3286913 0.0000007
## 5-4 0.69621286 0.339031802 1.0533939 0.0000025
Work Ability
## === DESCRIPTIVE STATISTICS ===
##
## Summary of work_ability :
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 7.000 8.000 7.469 9.000 10.000
##
## Summary of WHOQOL_Score :
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.091 3.091 3.591 3.539 4.000 4.955
##
## === CORRELATION ANALYSIS ===
##
## Pearson's correlation (parametric):
##
## Pearson's product-moment correlation
##
## data: x and y
## t = 9.8451, df = 177, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4910122 0.6820134
## sample estimates:
## cor
## 0.594845
##
##
## Spearman's correlation (non-parametric):
##
## Spearman's rank correlation rho
##
## data: x and y
## S = 383560, p-value < 2.2e-16
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.5987277
##
##
## === LINEAR REGRESSION ===
##
## Model Summary:
##
## Call:
## lm(formula = as.formula(paste(y_var, "~", x_var)), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.67183 -0.28438 -0.00948 0.37580 1.44940
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.00297 0.16081 12.455 <2e-16 ***
## work_ability 0.20563 0.02089 9.845 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.522 on 177 degrees of freedom
## Multiple R-squared: 0.3538, Adjusted R-squared: 0.3502
## F-statistic: 96.93 on 1 and 177 DF, p-value: < 2.2e-16
##
##
## ANOVA:
## Analysis of Variance Table
##
## Response: WHOQOL_Score
## Df Sum Sq Mean Sq F value Pr(>F)
## work_ability 1 26.410 26.4098 96.926 < 2.2e-16 ***
## Residuals 177 48.228 0.2725
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1## === DESCRIPTIVE STATISTICS ===
##
## Summary of work_ability :
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 7.000 8.000 7.469 9.000 10.000
##
## Summary of OHQ_Score :
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.828 3.103 3.552 3.636 4.224 5.414
##
## === CORRELATION ANALYSIS ===
##
## Pearson's correlation (parametric):
##
## Pearson's product-moment correlation
##
## data: x and y
## t = 4.8648, df = 177, p-value = 2.521e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2071878 0.4665935
## sample estimates:
## cor
## 0.3434239
##
##
## Spearman's correlation (non-parametric):
##
## Spearman's rank correlation rho
##
## data: x and y
## S = 601911, p-value = 3.361e-07
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
## rho
## 0.3702933
##
##
## === LINEAR REGRESSION ===
##
## Model Summary:
##
## Call:
## lm(formula = as.formula(paste(y_var, "~", x_var)), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.74511 -0.47459 -0.02632 0.50296 2.12499
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.5858 0.2225 11.620 < 2e-16 ***
## work_ability 0.1406 0.0289 4.865 2.52e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7223 on 177 degrees of freedom
## Multiple R-squared: 0.1179, Adjusted R-squared: 0.113
## F-statistic: 23.67 on 1 and 177 DF, p-value: 2.521e-06
##
##
## ANOVA:
## Analysis of Variance Table
##
## Response: OHQ_Score
## Df Sum Sq Mean Sq F value Pr(>F)
## work_ability 1 12.348 12.3480 23.667 2.521e-06 ***
## Residuals 177 92.349 0.5217
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Cluster Analysis
##
## Clustering method used: LCA
## # A tibble: 3 × 6
## cluster work_ability_mean WHOQOL_mean OHQ_mean work_experience_mean n
## <int> <dbl> <dbl> <dbl> <dbl> <int>
## 1 1 8.72 4.20 4.30 17.9 61
## 2 2 7.36 3.58 3.63 19.0 59
## 3 3 6.38 2.78 2.90 22.4 52| Cluster | N | OHQ Mean | WHOQOL Mean | Work Ability | Negative Feelings | Percent |
|---|---|---|---|---|---|---|
| Cluster 1: High Well-being | 11 | 4.581542 | 4.413223 | 8.636364 | 1.272727 | 6.1 |
| Cluster 2: Moderate Well-being | 55 | 3.892259 | 3.829303 | 8.181818 | 2.000000 | 30.7 |
| Cluster 3: Low Well-being | 74 | 3.620668 | 3.243117 | 6.851351 | 3.364865 | 41.3 |
| Cluster 4: Mixed Profile | 39 | 3.037009 | 3.443906 | 7.307692 | 3.512821 | 21.8 |
| Country | Cluster | N | OHQ Mean | WHOQOL Mean | Work Ability | Negative Feelings | Percent |
|---|---|---|---|---|---|---|---|
| Canada | Cluster 1: High Well-being | 6 | 4.860632 | 4.416667 | 8.833333 | 1.166667 | 7.7 |
| Canada | Cluster 2: Moderate Well-being | 25 | 3.890260 | 3.844121 | 8.400000 | 2.000000 | 32.1 |
| Canada | Cluster 3: Low Well-being | 31 | 3.512223 | 3.116555 | 6.741936 | 3.290323 | 39.7 |
| Canada | Cluster 4: Mixed Profile | 16 | 3.063962 | 3.518669 | 7.250000 | 3.437500 | 20.5 |
| United States | Cluster 1: High Well-being | 5 | 4.246634 | 4.409091 | 8.400000 | 1.400000 | 5.0 |
| United States | Cluster 2: Moderate Well-being | 30 | 3.893924 | 3.816955 | 8.000000 | 2.000000 | 29.7 |
| United States | Cluster 3: Low Well-being | 43 | 3.698849 | 3.334360 | 6.930233 | 3.418605 | 42.6 |
| United States | Cluster 4: Mixed Profile | 23 | 3.018259 | 3.391897 | 7.347826 | 3.565217 | 22.8 |
| Country | Cluster | N | OHQ Mean | OHQ SD | WHOQOL Mean | WHOQOL SD | Work Ability Mean | Work Ability SD | Negative Feelings Mean | Negative Feelings SD | Percent |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Canada | Cluster 1: High Well-being | 6 | 4.861 | 0.592 | 4.417 | 0.243 | 8.833 | 0.983 | 1.167 | 0.408 | 7.7 |
| Canada | Cluster 2: Moderate Well-being | 25 | 3.890 | 0.561 | 3.844 | 0.425 | 8.400 | 0.913 | 2.000 | 0.000 | 32.1 |
| Canada | Cluster 3: Low Well-being | 31 | 3.512 | 0.832 | 3.117 | 0.664 | 6.742 | 2.309 | 3.290 | 0.824 | 39.7 |
| Canada | Cluster 4: Mixed Profile | 16 | 3.064 | 0.522 | 3.519 | 0.716 | 7.250 | 2.082 | 3.438 | 0.727 | 20.5 |
| United States | Cluster 1: High Well-being | 5 | 4.247 | 0.749 | 4.409 | 0.345 | 8.400 | 1.517 | 1.400 | 0.548 | 5.0 |
| United States | Cluster 2: Moderate Well-being | 30 | 3.894 | 0.439 | 3.817 | 0.330 | 8.000 | 0.983 | 2.000 | 0.000 | 29.7 |
| United States | Cluster 3: Low Well-being | 43 | 3.699 | 0.834 | 3.334 | 0.686 | 6.930 | 2.219 | 3.419 | 0.852 | 42.6 |
| United States | Cluster 4: Mixed Profile | 23 | 3.018 | 0.444 | 3.392 | 0.468 | 7.348 | 1.584 | 3.565 | 0.662 | 22.8 |
## ## Main Test Results (ANOVA/Kruskal-Wallis)| statistic | p.value | Country | Variable | Test | Effect.Size | Effect.Size.Interpretation |
|---|---|---|---|---|---|---|
| 11.7758 | 0.0000 | Canada | OHQ_Score | ANOVA | 0.3231 | Large |
| 12.0589 | 0.0000 | Canada | WHOQOL_Score | ANOVA | 0.3284 | Large |
| 13.9794 | 0.0029 | Canada | work_ability | Kruskal-Wallis | 0.1816 | Large |
| 53.7083 | 0.0000 | Canada | negative_feelings_frequency | Kruskal-Wallis | 0.6975 | Large |
| 10.0023 | 0.0000 | United States | OHQ_Score | ANOVA | 0.2363 | Large |
| 9.6264 | 0.0000 | United States | WHOQOL_Score | ANOVA | 0.2294 | Large |
| 5.7209 | 0.1260 | United States | work_ability | Kruskal-Wallis | 0.0572 | Small |
| 66.0985 | 0.0000 | United States | negative_feelings_frequency | Kruskal-Wallis | 0.6610 | Large |
##
## ## Significant Post-hoc Comparisons| Comparison | diff | lwr | upr | p adj | Country | Variable | Test | Z | P.unadj | P.adj |
|---|---|---|---|---|---|---|---|---|---|---|
| Cluster 2: Moderate Well-being-Cluster 1: High Well-being | -0.9704 | -1.7825 | -0.1583 | 0.0127 | Canada | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 3: Low Well-being-Cluster 1: High Well-being | -1.3484 | -2.1452 | -0.5517 | 0.0002 | Canada | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 1: High Well-being | -1.7967 | -2.6518 | -0.9415 | 0.0000 | Canada | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 3: Low Well-being-Cluster 2: Moderate Well-being | -0.3780 | -0.8582 | 0.1022 | 0.1728 | Canada | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 2: Moderate Well-being | -0.8263 | -1.3982 | -0.2544 | 0.0017 | Canada | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 3: Low Well-being | -0.4483 | -0.9982 | 0.1016 | 0.1494 | Canada | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 2: Moderate Well-being-Cluster 1: High Well-being | -0.5725 | -1.2745 | 0.1294 | 0.1490 | Canada | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 3: Low Well-being-Cluster 1: High Well-being | -1.3001 | -1.9888 | -0.6115 | 0.0000 | Canada | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 1: High Well-being | -0.8980 | -1.6372 | -0.1588 | 0.0109 | Canada | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 3: Low Well-being-Cluster 2: Moderate Well-being | -0.7276 | -1.1426 | -0.3125 | 0.0001 | Canada | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 2: Moderate Well-being | -0.3255 | -0.8198 | 0.1689 | 0.3155 | Canada | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 3: Low Well-being | 0.4021 | -0.0732 | 0.8774 | 0.1263 | Canada | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | NA | NA | NA | NA | Canada | work_ability | Dunn’s Post-hoc | 0.6657 | 0.5056 | 0.6067 |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | NA | NA | NA | NA | Canada | work_ability | Dunn’s Post-hoc | 2.5799 | 0.0099 | 0.0296 |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | NA | NA | NA | NA | Canada | work_ability | Dunn’s Post-hoc | 3.1547 | 0.0016 | 0.0096 |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | Canada | work_ability | Dunn’s Post-hoc | 1.9795 | 0.0478 | 0.0716 |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | Canada | work_ability | Dunn’s Post-hoc | 2.0146 | 0.0439 | 0.0879 |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | Canada | work_ability | Dunn’s Post-hoc | -0.6595 | 0.5095 | 0.5095 |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | NA | NA | NA | NA | Canada | negative_feelings_frequency | Dunn’s Post-hoc | -1.5353 | 0.1247 | 0.1496 |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | NA | NA | NA | NA | Canada | negative_feelings_frequency | Dunn’s Post-hoc | -4.8345 | 0.0000 | 0.0000 |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | NA | NA | NA | NA | Canada | negative_feelings_frequency | Dunn’s Post-hoc | -5.4248 | 0.0000 | 0.0000 |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | Canada | negative_feelings_frequency | Dunn’s Post-hoc | -4.8692 | 0.0000 | 0.0000 |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | Canada | negative_feelings_frequency | Dunn’s Post-hoc | -5.1006 | 0.0000 | 0.0000 |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | Canada | negative_feelings_frequency | Dunn’s Post-hoc | -0.5676 | 0.5703 | 0.5703 |
| Cluster 2: Moderate Well-being-Cluster 1: High Well-being | -0.3527 | -1.1775 | 0.4721 | 0.6794 | United States | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 3: Low Well-being-Cluster 1: High Well-being | -0.5478 | -1.3546 | 0.2590 | 0.2916 | United States | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 1: High Well-being | -1.2284 | -2.0709 | -0.3859 | 0.0014 | United States | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 3: Low Well-being-Cluster 2: Moderate Well-being | -0.1951 | -0.6013 | 0.2111 | 0.5933 | United States | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 2: Moderate Well-being | -0.8757 | -1.3489 | -0.4024 | 0.0000 | United States | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 3: Low Well-being | -0.6806 | -1.1217 | -0.2395 | 0.0006 | United States | OHQ_Score | TukeyHSD | NA | NA | NA |
| Cluster 2: Moderate Well-being-Cluster 1: High Well-being | -0.5921 | -1.2731 | 0.0888 | 0.1115 | United States | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 3: Low Well-being-Cluster 1: High Well-being | -1.0747 | -1.7408 | -0.4086 | 0.0003 | United States | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 1: High Well-being | -1.0172 | -1.7128 | -0.3216 | 0.0013 | United States | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 3: Low Well-being-Cluster 2: Moderate Well-being | -0.4826 | -0.8180 | -0.1472 | 0.0016 | United States | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 2: Moderate Well-being | -0.4251 | -0.8158 | -0.0343 | 0.0274 | United States | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 4: Mixed Profile-Cluster 3: Low Well-being | 0.0575 | -0.3066 | 0.4217 | 0.9761 | United States | WHOQOL_Score | TukeyHSD | NA | NA | NA |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | NA | NA | NA | NA | United States | negative_feelings_frequency | Dunn’s Post-hoc | -0.8896 | 0.3737 | 0.4484 |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | NA | NA | NA | NA | United States | negative_feelings_frequency | Dunn’s Post-hoc | -4.2043 | 0.0000 | 0.0000 |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | NA | NA | NA | NA | United States | negative_feelings_frequency | Dunn’s Post-hoc | -6.5445 | 0.0000 | 0.0000 |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | United States | negative_feelings_frequency | Dunn’s Post-hoc | -4.4031 | 0.0000 | 0.0000 |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | United States | negative_feelings_frequency | Dunn’s Post-hoc | -6.2888 | 0.0000 | 0.0000 |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | NA | NA | NA | NA | United States | negative_feelings_frequency | Dunn’s Post-hoc | -0.7204 | 0.4713 | 0.4713 |
Cmparing Clusters
| Country | Cluster | N | OHQ Mean | WHOQOL Mean | Work Ability | Negative Feelings | Percent |
|---|---|---|---|---|---|---|---|
| Canada | Cluster 1: High Well-being | 6 | 3.316 | 2.758 | 2.500 | 3.167 | 7.7 |
| Canada | Cluster 2: Moderate Well-being | 20 | 2.956 | 3.415 | 7.600 | 3.550 | 25.6 |
| Canada | Cluster 3: Low Well-being | 25 | 3.923 | 3.890 | 8.440 | 2.000 | 32.1 |
| Canada | Cluster 4: Mixed Profile | 27 | 3.972 | 3.460 | 7.778 | 2.741 | 34.6 |
| United States | Cluster 1: High Well-being | 10 | 4.791 | 4.332 | 8.800 | 2.800 | 9.9 |
| United States | Cluster 2: Moderate Well-being | 27 | 4.063 | 3.918 | 8.074 | 1.926 | 26.7 |
| United States | Cluster 3: Low Well-being | 46 | 3.353 | 3.239 | 6.609 | 3.283 | 45.5 |
| United States | Cluster 4: Mixed Profile | 18 | 3.036 | 3.327 | 7.722 | 3.611 | 17.8 |
## ## Kruskal-Wallis Test Results by Country| Country | Variable | Test | Statistic | p.value | Effect.Size |
|---|---|---|---|---|---|
| Canada | OHQ_Score | Kruskal-Wallis | 20.8910 | 0.0001 | 0.2713 |
| Canada | WHOQOL_Score | Kruskal-Wallis | 13.4554 | 0.0037 | 0.1747 |
| Canada | work_ability | Kruskal-Wallis | 21.4955 | 0.0001 | 0.2792 |
| Canada | negative_feelings_frequency | Kruskal-Wallis | 34.3234 | 0.0000 | 0.4458 |
| United States | OHQ_Score | Kruskal-Wallis | 57.3687 | 0.0000 | 0.5737 |
| United States | WHOQOL_Score | Kruskal-Wallis | 46.2710 | 0.0000 | 0.4627 |
| United States | work_ability | Kruskal-Wallis | 16.3862 | 0.0009 | 0.1639 |
| United States | negative_feelings_frequency | Kruskal-Wallis | 48.7837 | 0.0000 | 0.4878 |
##
## ## Significant Post-hoc Comparisons (Dunn's Test)| Comparison | Z | P.unadj | P.adj | Country | Variable | Test |
|---|---|---|---|---|---|---|
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | 0.5887 | 0.5561 | 0.6673 | Canada | OHQ_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | -1.9540 | 0.0507 | 0.1014 | Canada | OHQ_Score | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | -3.8744 | 0.0001 | 0.0006 | Canada | OHQ_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | -1.9232 | 0.0545 | 0.0817 | Canada | OHQ_Score | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | -3.8710 | 0.0001 | 0.0003 | Canada | OHQ_Score | Dunn’s Post-hoc |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | 0.0731 | 0.9417 | 0.9417 | Canada | OHQ_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | -1.9915 | 0.0464 | 0.0557 | Canada | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | -3.4208 | 0.0006 | 0.0037 | Canada | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | -2.0938 | 0.0363 | 0.0544 | Canada | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | -2.1399 | 0.0324 | 0.0971 | Canada | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | -0.1316 | 0.8953 | 0.8953 | Canada | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | 2.1232 | 0.0337 | 0.0675 | Canada | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | -3.2783 | 0.0010 | 0.0021 | Canada | work_ability | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | -4.6021 | 0.0000 | 0.0000 | Canada | work_ability | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | -1.8872 | 0.0591 | 0.0887 | Canada | work_ability | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | -3.6067 | 0.0003 | 0.0009 | Canada | work_ability | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | -0.3452 | 0.7300 | 0.7300 | Canada | work_ability | Dunn’s Post-hoc |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | 1.6729 | 0.0944 | 0.1132 | Canada | work_ability | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | -1.0580 | 0.2901 | 0.3481 | Canada | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | 2.7039 | 0.0069 | 0.0137 | Canada | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | 5.7389 | 0.0000 | 0.0000 | Canada | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | 0.6444 | 0.5193 | 0.5193 | Canada | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | 2.6550 | 0.0079 | 0.0119 | Canada | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | -3.3809 | 0.0007 | 0.0022 | Canada | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | 2.1880 | 0.0287 | 0.0344 | United States | OHQ_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | 5.3409 | 0.0000 | 0.0000 | United States | OHQ_Score | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | 4.3456 | 0.0000 | 0.0000 | United States | OHQ_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | 6.1887 | 0.0000 | 0.0000 | United States | OHQ_Score | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | 5.3597 | 0.0000 | 0.0000 | United States | OHQ_Score | Dunn’s Post-hoc |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | 2.0767 | 0.0378 | 0.0378 | United States | OHQ_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | 1.7805 | 0.0750 | 0.0900 | United States | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | 5.2820 | 0.0000 | 0.0000 | United States | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | 4.8829 | 0.0000 | 0.0000 | United States | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | 4.6169 | 0.0000 | 0.0000 | United States | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | 3.8180 | 0.0001 | 0.0002 | United States | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | -0.0792 | 0.9369 | 0.9369 | United States | WHOQOL_Score | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | 1.3334 | 0.1824 | 0.2189 | United States | work_ability | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | 3.4377 | 0.0006 | 0.0035 | United States | work_ability | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | 2.9114 | 0.0036 | 0.0108 | United States | work_ability | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | 1.8245 | 0.0681 | 0.1362 | United States | work_ability | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | 0.7426 | 0.4577 | 0.4577 | United States | work_ability | Dunn’s Post-hoc |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | -1.7260 | 0.0843 | 0.1265 | United States | work_ability | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 2: Moderate Well-being | 2.8131 | 0.0049 | 0.0098 | United States | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 3: Low Well-being | -1.1276 | 0.2595 | 0.2595 | United States | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 3: Low Well-being | -5.9182 | 0.0000 | 0.0000 | United States | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 1: High Well-being - Cluster 4: Mixed Profile | -2.1245 | 0.0336 | 0.0504 | United States | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 2: Moderate Well-being - Cluster 4: Mixed Profile | -6.1759 | 0.0000 | 0.0000 | United States | negative_feelings_frequency | Dunn’s Post-hoc |
| Cluster 3: Low Well-being - Cluster 4: Mixed Profile | -1.5988 | 0.1099 | 0.1318 | United States | negative_feelings_frequency | Dunn’s Post-hoc |
